In [1]:
# sources
# - https://bbbc.broadinstitute.org/BBBC005
# - https://bbbc.broadinstitute.org/search/synthetic%20cells?
# - https://www.geeksforgeeks.org/distance-transformation-in-image-python-opencv/
# - https://www.geeksforgeeks.org/how-to-adjust-the-position-of-a-matplotlib-colorbar/
# - https://opencv24-python-tutorials.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_watershed/py_watershed.html
# - https://scikit-image.org/docs/stable/auto_examples/segmentation/plot_watershed.html
# - https://stackoverflow.com/questions/70361424/extract-white-regions-from-binary-images
# - https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.binary_fill_holes.html
# - https://www.tutorialspoint.com/how-to-compute-the-area-and-perimeter-of-an-image-contour-using-opencv-python
# - https://www.youtube.com/watch?v=WQpXS9gBEu8
# data source
# Synthetic cells, Accession number BBBC005 · Version 1
# A. Lehmussola, P. Ruusuvuori, J. Selinummi, H. Huttunen and O. Yli-Harja,
# "Computational Framework for Simulating Fluorescence Microscope Images With Cell Populations,"
# in IEEE Transactions on Medical Imaging, vol. 26, no. 7, pp. 1010-1016, July 2007, doi: 10.1109/TMI.2007.896925.
#
# A. Lehmussola, P. Ruusuvuori, J. Selinummi, T. Rajala and O. Yli-Harja,
# "Synthetic Images of High-Throughput Microscopy for Validation of Image Analysis Methods,"
# in Proceedings of the IEEE, vol. 96, no. 8, pp. 1348-1360, Aug. 2008, doi: 10.1109/JPROC.2008.925490.
#
# - https://bbbc.broadinstitute.org/BBBC005
# - https://bbbc.broadinstitute.org/search/synthetic%20cells?
# data are published under CC0 1.0 Universal (CC0 1.0), https://creativecommons.org/publicdomain/zero/1.0/
# import modules
import cv2
from matplotlib import cm
from matplotlib import pyplot as plt
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np
from scipy import ndimage
# initialize and define variables and parameter
# fileName = "SIMCEPImages_A23_C96_F1_s19_w1.TIF" # mask
fileName = "SIMCEPImages_A23_C96_F1_s19_1_real.TIF" # "real" image
cellCountTruth = int(fileName[fileName.find('_C')+2:fileName.find('_F')]) # extract real cell number from filename
myThresholdValue = 70 # treshold value for binary mask
myImage = cv2.imread('./Data/' + fileName) # read image
myImageGray = cv2.cvtColor(myImage, cv2.COLOR_BGR2GRAY)
# ret, thresh = cv2.threshold(gray, 0, 1, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
# fill holes
# define binary mask on computed on thresholded image
myImageThresholded = np.zeros(myImageGray.shape)
myImageThresholded[myImageGray > myThresholdValue] = 1
myImageThresholded = ndimage.binary_fill_holes(myImageThresholded).astype(int)
# plot figure
fig, ax = plt.subplots(figsize = (15, 7))
im = ax.imshow(myImageThresholded, cmap = cm.coolwarm)
divider = make_axes_locatable(ax)
cax = divider.new_vertical(size = "5%", pad = 0.3, pack_start = True)
fig.add_axes(cax)
cbar = fig.colorbar(im, cax = cax, ticks = [-1, 0, 1], orientation = 'horizontal')
TitleString = "\"Synthetic cells\": Binary Mask\n"
plt.title(TitleString, fontweight="bold", fontsize = 16, y = 22.3)
plt.suptitle("File: " + fileName, fontsize = 10, y = 0.91)
ax.set_xlabel("binary mask color scheme")
ax.set_xticks([])
ax.set_yticks([])
plt.show()
